Belmekki Mohamed Amir Alaa, Leach Jonathan, Tobin Rachael, Buller Gerald S, McLaughlin Stephen, Halimi Abderrahim
Opt Express. 2023 Jul 17;31(15):23729-23745. doi: 10.1364/OE.487896.
3D single-photon LiDAR imaging has an important role in many applications. However, full deployment of this modality will require the analysis of low signal to noise ratio target returns and very high volume of data. This is particularly evident when imaging through obscurants or in high ambient background light conditions. This paper proposes a multiscale approach for 3D surface detection from the photon timing histogram to permit a significant reduction in data volume. The resulting surfaces are background-free and can be used to infer depth and reflectivity information about the target. We demonstrate this by proposing a hierarchical Bayesian model for 3D reconstruction and spectral classification of multispectral single-photon LiDAR data. The reconstruction method promotes spatial correlation between point-cloud estimates and uses a coordinate gradient descent algorithm for parameter estimation. Results on simulated and real data show the benefits of the proposed target detection and reconstruction approaches when compared to state-of-the-art processing algorithms.
三维单光子激光雷达成像在许多应用中发挥着重要作用。然而，要全面部署这种模式，需要分析低信噪比的目标回波以及处理非常大量的数据。当透过障碍物成像或在高环境背景光条件下成像时，这一点尤为明显。本文提出了一种从光子定时直方图进行三维表面检测的多尺度方法，以大幅减少数据量。所得表面无背景干扰，可用于推断目标的深度和反射率信息。我们通过提出一种用于多光谱单光子激光雷达数据的三维重建和光谱分类的分层贝叶斯模型来证明这一点。该重建方法促进了点云估计之间的空间相关性，并使用坐标梯度下降算法进行参数估计。与现有最先进的处理算法相比，模拟数据和真实数据的结果显示了所提出的目标检测和重建方法的优势。
